Evidence map›Paper›PMID 24376861›Full record

ArticlePloS one2013

An extensive evaluation of read trimming effects on Illumina NGS data analysis.

Cristian Del Fabbro, Simone Scalabrin, Michele Morgante, Federico M Giorgi

Abstract readComparative StudyEvaluation Study
In one paragraph

Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 218 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
218citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

218 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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158 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Cristian Del FabbroInstitute of Applied Genomics, Udine, Italy.
Simone ScalabrinIGA Technology Services, Udine, Italy.
Michele MorganteInstitute of Applied Genomics, Udine, Italy.
Federico M GiorgiInstitute of Applied Genomics, Udine, Italy ; Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Next Generation Sequencing is having an extremely strong impact in biological and medical research and diagnostics, with applications ranging from gene expression quantification to genotyping and genome reconstruction. Sequencing data is often provided as raw reads which are processed prior to analysis 1 of the most used preprocessing procedures is read trimming, which aims at removing low quality portions while preserving the longest high quality part of a NGS read. In the current work, we evaluate nine different trimming algorithms in four datasets and three common NGS-based applications (RNA-Seq, SNP calling and genome assembly). Trimming is shown to increase the quality and reliability of the analysis, with concurrent gains in terms of execution time and computational resources needed.

Indexed as

AlgorithmsComputational BiologyHigh-Throughput Nucleotide SequencingResearch Design

Identifiers

PMID24376861
PMCPMC3871669

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.